Results 81 to 90 of about 154,891 (246)
Enhancing diabetes risk prediction through focal active learning and machine learning models
To improve the effectiveness of diabetes risk prediction, this study proposes a novel method based on focal active learning strategies combined with machine learning models.
Wangyouchen Zhang +4 more
doaj +2 more sources
Functional and Structural Evidence of Neurofluid Circuit Aberrations in Huntington Disease
ABSTRACT Objective Disrupted neurofluid regulation may contribute to neurodegeneration in Huntington disease (HD). Because neurofluid pathways influence waste clearance, inflammation, and the distribution of central nervous system (CNS)–delivered therapeutics, understanding their dysfunction is increasingly important as targeted treatments emerge.
Kilian Hett +8 more
wiley +1 more source
A Depolarizing Leak in Sodium Bicarbonate Cotransporter NBCe1 Causes Brain Edema
ABSTRACT Objectives SLC4A4 encodes electrogenic sodium bicarbonate cotransporter NBCe1, prominently expressed in kidney and brain. Recessive loss‐of‐function variants in SLC4A4 cause proximal renal tubular acidosis, no brain edema. In the brain, NBCe1 is expressed by astrocytes, where it regulates pH and mediates astrocyte volume changes.
Quinty Bisseling +16 more
wiley +1 more source
Convergence between emerging technologies and active methodologies in the university
In today's educational environment, the convergence of emerging technologies and active methodologies has become a fundamental driver of change in university education.
Oscar-Yecid Aparicio-Gómez +2 more
doaj +1 more source
Objective The purpose was to evaluate a biomarker score consisting of MUC5B rs35705950 promoter variant, plasma matrix metalloproteinase‐7 (MMP‐7), and serum anti–malondialdehyde‐acetaldehyde (anti‐MAA) antibody for rheumatoid arthritis (RA)–associated interstitial lung disease (ILD) risk stratification.
Kelsey Coziahr +16 more
wiley +1 more source
Machine learning phases of active matter
5 pages, 4 ...
Xue, Tingting +4 more
openaire +2 more sources
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Active Learning for Machine Learning Driven Molecular Dynamics
9 pages, 4 figures, for Neurips Workshop: Machine Learning and the Physical Sciences ...
Bachelor, Kevin +3 more
openaire +2 more sources
The documentation of component manufacture has become an essential part of today's production processes, especially for the analysis and optimization of production or component design with regard to structural performance, economic efficiency, and sustainability.
Björn Denker +4 more
wiley +1 more source
Active Learning for Stacking and AdaBoost-Related Models
Ensemble learning (EL) has become an essential technique in machine learning that can significantly enhance the predictive performance of basic models, but it also comes with an increased cost of computation.
Qun Sui, Sujit K. Ghosh
doaj +1 more source

